 
In order to evaluate, when compared to conventional deep learning components (i.e., convolutional and recurrent layers), whether our proposed STFNet component is better at decoding information and extracting features from sensing inputs, we substitute components in the state-of-the-art neural network structure for IoT applications with STFNet. In the whole evaluation, we choose DeepSense as the state-of-the-art structure, which has shown signifiant improvements on various sensing tasks [1]}. The illustration of structures of five comparing models with two sensor inputs are shown in Table REF . Detailed information of our comparing models are listed as follows,
